Motivated by the development of 5G massive MTC and large-scale distributed 5G C-RAN architecture, in this paper, we proposed a scalable and efficient linear state estimation framework. The proposed framework is based on the GBP algorithm and jointly combines linear state estimation with signal detection in 5G C-RANs. The advantage of GBP solution is accuracy that matches the MMSE estimation, low complexity due to lack of scheduling MTC-UE transmissions, low latency due to simultaneous data transfer, scalability to large-scale systems (due to the fact that the underlying factor graph is usually sparse), and ease of parallelization and distributed implementation in future distributed F-RAN architectures. For the future work, we aim to provide rigorous convergence analysis of GBP in the presented framework, motivated by similar analysis in [1]} and [2]}, and provide extensive numerical simulation study.
